Timing of flowering affects pollination of Viburnum edule in Alaskan boreal forest
Bibliographic record
Abstract
Flowering time in Alaskan boreal forest is advancing, and this may affect pollination rates of early-flowering species. Viburnum edule is one of the first understory plants to flower, when pollinator diversity and abundance are likely lower than later in the season. We evaluated the impact of flowering time on pollen deposition and composition of the pollinator community over two years (one in which plants flowered slightly earlier than average and one in which flowering time was close to average) using experimental arrays with branches that flowered either at the start or the peak of flowering for each year. Pollinator exclusion reduced fruit set by > 90%, but even plants freely pollinated by insects had fruit set rates of < 10%. Both within and across years, plants that flowered later had more insect visitors and higher proportions of stigmas visited (> 5 pollen grains per stigma); in the advanced year, plants that flowered later also had more pollen grains per stigma. Syrphid flies, solitary bees, and muscid flies constituted ~ 99% of visitors, with a higher proportion of syrphid flies for later-flowering plants within and across years. Despite evidence for potential pollen limitation, pollen loads for the earliest flowering plants were high (mean > 25 pollen grains per stigma). Fruit production in V. edule is likely limited by inefficient pollen transfer between genets, by resource availability, or both. Given the prevalence of syrphid and muscid flies as pollinators, we need a better understanding of what triggers emergence in these taxa to evaluate the potential for trophic mismatches in boreal forest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".